Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network.
Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network.
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DOI：
10.1186/s13073-015-0181-z
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发表时间：
2015
期刊：
影响因子：
12.3
通讯作者：
Jarvik GP
中科院分区：
文献类型：
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作者：
Crosslin DR;Robertson PD;Carrell DS;Gordon AS;Hanna DS;Burt A;Fullerton SM;Scrol A;Ralston J;Leppig K;Hartzler A;Baldwin E;Andrade Md;Kullo IJ;Tromp G;Doheny KF;Ritchie MD;Crane PK;Nickerson DA;Larson EB;Jarvik GP
为了从变异数据返回可操作的结果到电子健康记录（EHRs），电子医疗记录和基因组学（eMERGE）网络的参与者正在使用靶向药物基因组学研究网络序列平台（PGRNseq）进行测序。这个具有成本效益，高度可扩展，高度精确的平台用于探索84个具有强药物表型关联的关键药物遗传基因的罕见变异。为了将临床实验室改进修订（CLIA）结果返回给我们在集团健康合作社的参与者，我们对900名参与者（61%为女性）的非CLIA生物库样本进行了DNA测序。然后，我们选择其中450人重新同意，重新抽血，并最终验证CLIA变体，预计将结果返回给参与者和电子病历。这450人是通过我们设计的一种算法来选择的，该算法利用了来自自我报告的种族、诊断和程序代码、医疗记录、实验室结果和变异水平生物信息学的数据，以确保选择一个信息丰富的样本。我们通过SeattleSeq和SnpEff工具的组合对多样本变体呼叫格式进行注释，并使用额外的自定义变量，包括来自ClinVar、OMIM、HGMD和先前临床关联的证据。我们集中分析了27个可操作的基因，主要由临床药物遗传学实施联盟推动。我们根据每个参与者的编码变异总数（75.2±14.7）和具有高或中度影响的编码变异数量（11.5±3.9）建立了一个排名系统。值得注意的是，在这27个基因的1785个变异中，我们发现了11个停止获得（1%）和519个错义（20%）变异。最后，我们将具有先前致病性临床证据的变异优先返回到EHR，或注释为以下基因的停止增益：CACNA1S和RYR1（恶性高热）；SCN5A、KCNH2、RYR2（心律失常）；LDLR（高胆固醇）将遗传学纳入电子病历以支持临床决策是一项复杂的工作，原因有很多，包括对返回结果缺乏事先同意，缺乏在CLIA环境中收集的生物标本，以及电子病历的整合。我们的研究设计考虑了这些障碍，是一个试点系统的例子，可以在扩展到整个卫生系统之前加以利用。本文的在线版本（doi:10.1186/s13073-015-0181-z）包含补充材料，授权用户可以使用。
In an effort to return actionable results from variant data to electronic health records (EHRs), participants in the Electronic Medical Records and Genomics (eMERGE) Network are being sequenced with the targeted Pharmacogenomics Research Network sequence platform (PGRNseq). This cost-effective, highly-scalable, and highly-accurate platform was created to explore rare variation in 84 key pharmacogenetic genes with strong drug phenotype associations. To return Clinical Laboratory Improvement Amendments (CLIA) results to our participants at the Group Health Cooperative, we sequenced the DNA of 900 participants (61 % female) with non-CLIA biobanked samples. We then selected 450 of those to be re-consented, to redraw blood, and ultimately to validate CLIA variants in anticipation of returning the results to the participant and EHR. These 450 were selected using an algorithm we designed to harness data from self-reported race, diagnosis and procedure codes, medical notes, laboratory results, and variant-level bioinformatics to ensure selection of an informative sample. We annotated the multi-sample variant call format by a combination of SeattleSeq and SnpEff tools, with additional custom variables including evidence from ClinVar, OMIM, HGMD, and prior clinical associations. We focused our analyses on 27 actionable genes, largely driven by the Clinical Pharmacogenetics Implementation Consortium. We derived a ranking system based on the total number of coding variants per participant (75.2±14.7), and the number of coding variants with high or moderate impact (11.5±3.9). Notably, we identified 11 stop-gained (1 %) and 519 missense (20 %) variants out of a total of 1785 in these 27 genes. Finally, we prioritized variants to be returned to the EHR with prior clinical evidence of pathogenicity or annotated as stop-gain for the following genes: CACNA1S and RYR1 (malignant hyperthermia); SCN5A, KCNH2, and RYR2 (arrhythmia); and LDLR (high cholesterol). The incorporation of genetics into the EHR for clinical decision support is a complex undertaking for many reasons including lack of prior consent for return of results, lack of biospecimens collected in a CLIA environment, and EHR integration. Our study design accounts for these hurdles and is an example of a pilot system that can be utilized before expanding to an entire health system. The online version of this article (doi:10.1186/s13073-015-0181-z) contains supplementary material, which is available to authorized users.